similar to: Fitted values and residuals from glmmPQL (MASS package)

Dear All, I carry out negative binomial estimations using the glm.nb command from the MASS package. Is there a command or a simple procedure for computing marginal effects from a glm.nb fitted object? If these are the same as for a Poisson fitted object (glm), my question remains how to compute them. Thanks in advance for your help. Roberto Patuelli ******************** Roberto Patuelli, Ph.D.

Dear All, I've been using already for a year glm.nb() from the MASS package. But today, R gave me an error message when estimating one of my usual models: > depEsf.nb <- glm.nb(depE ~ manuf00E + corps00E + lngdp00E + lngdp00sqE + > lnpop00E + indshE + scishE + mechshE + elecshE + chemshE + drugshE + > urban_dummyE + aggl_dummyE + + eE1 + eE2 + eE3 + eE4 + eE5 + eE6 + eE7 +

Dear ALL, I'm trying to figure out what the percentage effects are in a logistic regression. To be more clear, I'm not interested in the effect on y of a 1-unit increase in x, but on the percentage effect on y of a 1% increase in x (in economics this is also often called an "elasticity"). For example, if my independent variables are in logs, the betas can be directly

Dear All, I'm looking to perform an ANOVA between two nested panel fixed effects models. I tried with anova, as well as with waldtest. anova tells me there is no method available for plm objects, while waldtest tells me my models are not nested. I think they are instead. The difference between the two models is that in the second I let the regression coefficients of a given variable variate

Dear All, I was wondering if it is possible to generate a regression summary (it does not matter at this stage if from an lm or for example a glm estimate) in which to obtain the joint significance of a set of regressors? Examples could be looking at the joint significance level of a polynomial, or of a set of exogenous variables of which is of interest the linear combination suggested by the

Dear All, I'm trying to use waldtest to test poolability (parameter stability) between two logistic regressions. Because I need to use robust standard errors (using sandwich), I cannot use anova. anova has no problems running the test, but waldtest does, indipendently of specifying vcov or not. waldtest does not appear to see that my models are nested. H0 in my case is the the vector of

Hello everybody My collegue and I noticed a strange behaviour of R on different platforms. It's a simple computation, but results are rather different. On Windows XP: > floor(log(8,2)) [1] 3 which is what one should expect. Here's instead the result with Mac OS X (same version, 2.5.0 (2007-04-23)) > floor(log(8,2)) [1] 2 Is it a "bug" in R or in the operating

Estimados usarios de R: Tengo una base de datos madre en formato .sav de SPSS y la quiero modificar usando datos de otras base de datos .sav y otra en .csv a las que llamare hijos. No tengo problema en convertirlas en data.frame. Todos los archivos tienen en común una variable que es única. En aquellos casos que les falta un valor a una variable en el archivo madre lo relleno con el valor del

Dear Duncan, Thank you a lot for your feedback. I have a problem though. The branch weights counters overflow in some files and thus I get incorrect numbers. Is there any way to find a workaround for that? Is is supposed to be a known bug or is it something that needs configuration on my part? Again, thank you a lot for your reply. Best Regards, Georgios Zacharopoulos 2016-02-03 18:23

Dear Duncan, I am generating branch-weights annotated IR files as described in the documentation of LLVM, using profiling with instrumentation. http://clang.llvm.org/docs/UsersManual.html#profiling-with-instrumentation e.g. llvm-profdata merge -output=$(BENCH).profdata default.profraw > clang -S -emit-llvm -O3 -fprofile-instr-use=$(BENCH).profdata -o > bench.prof.ll bench.c The issue is

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Hello All, Specifying 'type = "response"' when using predict() on a model fit using glm(...,family="binomial") returns fitted probabilities. Is it possible to get the same from a model object fit using glmmPQL() ? Thanks in advance, Rob _____________________________________________________ Rob Keefe Lab: (208) 885-5165 M.S. student

I'm a bit puzzled by how to write out additive random effects in glmmPQL. In my situation, I have a factorial design on two (categorical) random factors, A and B. At each combination, I have a binary response, y, and two binary fixed covariates, C and D. If everything were fixed, I would use glm(y ~ A + B + C + D, family = binomial) My first thought was to use glmmPQL(y ~ A + B, random

Hello, I am using the R function glmmPQL to fit a logistic GLMM, with weights. I am finding that I get fairly different parameter estimates in glmmPQL from fitting the full dataset (with no "weight" statement) and an equivalent, shorter dataset with the weights statement. I am using the weights statement in the 'glmmPQL' function exactly as in the 'glm' function. I

Hi ! I try to compare some models obtained from glmmPQL. model1 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 +I(freq8_4^2), random=~1|num, binomial); model2 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 , random=~1|num, binomial); anova(model1, model2) here is the answer : Erreur dans anova.lme(model1, model2) : Objects must

Dear all, for a cross-validation I have to use predict.glmmPQL() , where the formula of the corresponding glmmPQL call is not given explicitly, but constructed using as.formula. However, this does not work as expected: x1<-rnorm(100); x2<-rbinom(100,3,0.5); y<-rpois(100,2) mydata<-data.frame(x1,x2,y) library(MASS) # works as expected model1<-glmmPQL(y~x1, ~1 | factor(x2),

I have come across the previous communication on this list in September (copied below) because I had received the same error message. I understand from Brian Ripley's reply that anova should not be used with glmmPQL because it is not an adequate method, and that this is now shown with an error message. My question is, what method *should* be used? Using summary does not give me the result

Dear R-help, I recently tried implementing glmmPQL in 2.3.1, and I discovered a few differences as compared to 2.2.1. I am fitting a regression with fixed and random effects with Gamma error structure. First, 2.3.1 gives different estimates than 2.2.1, and 2.3.1, takes more iterations to converge. Second, when I try using the anova function it says, "'anova' is not available

Hi, In glmmPQL in the MASS library, the function uses repeated calls to the function lme(), using ML. Does anyone know how you can change this to REML? I know that in lme(), the default is actually set to REML and you can also specify this as 'method=REML' or 'method'ML' but this isn't applicable to glmmPQL(). I'd appreciate any help or advice! Thanks, Emma